RAG (Retrieval Augmented Generation) is a technique in Natural Language Processing (NLP) that enables Large Language Models like ChatGPT to generate customized outputs outside the scope of their training data by combining retrieval and generation capabilities. A RAG system consists of two primary components: the retriever, which searches through a knowledge base for relevant information, and the generator, which crafts prompts based on retrieval results to produce coherent responses. This technique has various applications across different fields, including customer support, data extraction, sales enablement, and content creation, allowing LLMs to produce more personalized and relevant outputs. A RAG-based LLM application can be built using Python, ChromaDB for vector database storage, and OpenAI for chat completion, providing a foundation for creating customized chatbots with enhanced response capabilities.